nick fleming

8/3/2019 Nick Fleming

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UNIVERSITY OF EDINBURGH

Business School

HONOURS YEAR DISSERTATION (2010/2011):

Drivers and inhibitors of mobile-banking adoption:Mobile-banking and its adoption amongstgeneration Y

consumers in the UK retail banking sector.

Candidate Name:

Nick Fleming

Matriculation Number:0786810

Submission Date:

Tuesday, 1st March, 2011

Advisor Name:Dr. Ashley Lloyd


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ABSTRACT

Nick Fleming (0786810)

Drivers and inhibitors of mobile-banking adoption

Mobile phones have become one of the most highly popular technological innovations in

recent years with high penetration rates in the UK. Customers can now access banking services

via their mobile device through SMS, mobile Internet browsers and mobile-applications, and

benefits are to be reaped by both customer, and bank. Ubiquity is a key benefit for customers as

they can access their bank services anytime, anywhere, at a time which is convenient to them.

Cost cutting, new customer acquisition, current customer retention and effective cross-sellingopportunities are key benefits pertaining to the employment of mobile-banking by retail banks in

their distribution portfolio. The recent growth in the smart-phone market was a key justifier for

this study, as research showed smart-phones have implications regarding the heightened

adoption of mobile-banking. Research showed that generation Y consumers (those aged

between 18 and 34) are a group of important consumers who are likely to be interested in

adopting m-banking, and hence were the focus for this study. With the utilisation of the

Technology Acceptance Model (TAM), this paper identified the key drivers and inhibitors for

consumer adoption of mobile phone banking, particularly those that affected the consumer's

attitude towards, and intention to use, this self-service banking technology. A quantitative survey

was conducted in the UK amongst generation Y consumers, to which 281 responses were

received. It was found that perceived usefulness, perceived ease of use, perceived self-efficacy,

perceived compatibility, perceived speed and perceived mobility had a significant positive

impact on consumers intention to adopt. Additionally, it was found that consumers mobile

competency, IT competency and electronic-banking competency were found to positively impact

their adoption intentions regarding m-banking. Perceived financial cost was identified as being

important to consumers but a significant majority did not perceive m-banking to be costly.

Perceived overall risk, perceived security and perceived privacy were found to be significant

issues that consumers flagged as key disadvantages to utilising m-banking.

Keywords: adoption of innovations, consumer behaviour, electronic-banking, financial services,

generation Y, innovation, mobile-banking, retail banking, smart-phone, Technology

Acceptance Model, technology, young consumers.


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ACKNOWLEDGMENTS

I would like to take this opportunity to thank my dissertation supervisor, Dr Ashley

Lloyd, whose guidance and feedback proved extremely useful to the successful completion of

this study. Additionally, I would like to sincerely thank all the respondents who participated in

the survey I administered for this study. The interesting results obtained from their quantitative

and qualitative feedback would not have been possible without them.


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TABLE OF CONTENTS

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii - v

TABLE OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi - vii

LIST OF ABBREVIATIONS / TERMINOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . viii

CHAPTER 1INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 M-bankinga definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Rationale & significance of study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Structure of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 - 4

CHAPTER 2LITERATURE REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Structure of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 A shift away from bricks and mortar: an overview of literature pertaining

to the adoption of electronic channels of banking . . . . . . . . . . . . . . . . . . . . . . . . . 6 - 9

2.3 Review of the UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Theoretical framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4.1 Technology Acceptance Model (TAM) . . . . . . . . . . . . . . . . . . . . . . . . 9 - 12

2.5 Prior studies which have extended the TAM: encompassing additional

constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 - 14

2.5.1 Self-efficacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5.2 Security (sub-variable of credibility) . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5.3 Privacy (sub-variable of credibility) . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5.4 Financial cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 - 16


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2.5.5 Adding constructs to Luarn & Lins extended TAM . . . . . . . . . . . . . 16 - 17

2.5.6 Research model creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

CHAPTER 3METHODOLOGY & RESEARCH GAP . . . . . . . . . . . . . . . . . . . . 18

3.1 Structure of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Research gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 - 20

3.3 Proposed research model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 - 23

3.4 Research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 - 25

3.4.1 Advantages of employing a questionnaire in data collection . . . . . . . 24

3.4.2 Disadvantages of employing a questionnaire in data collection . . . . . 25

3.5 Online survey design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 - 29

3.6 Data collection procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.6.1 Sampling methods employed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 - 31

3.6.2 Administering the online survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.7 Data preparation and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.8 Assumptions made in research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

CHAPTER 4DATA ANALYSIS, INTERPRETATION & DISCUSSION

OF FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.1Structure of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.2Survey sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3A descriptive overview of total respondents . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3.1Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 - 36

4.3.2Current usage of banking channels . . . . . . . . . . . . . . . . . . . . . . . . . . 37 - 38

4.3.3Respondents usage of mobile phones . . . . . . . . . . . . . . . . . . . . . . . 39

4.4Awareness of m-banking provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 - 42

4.5M-banking provision by UK retail banks: respondents perceived or

experienced satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 - 44

4.6Interest in adopting m-banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 - 46

4.7Testing for relationships between respondents characteristics and their

current usage of m-banking, and their interest in using m-banking . . . . . . . . . . . 46 - 47

4.7.1Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 (A3)

4.7.2Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 (A3)


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4.7.3Use of Internet banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.7.4Owning a smart-phone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 - 49

4.8Perceptions of m-banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 - 50

4.8.1General advantages of m-banking . . . . . . . . . . . . . . . . . . . . . . . . . . 50 - 51

4.8.2General disadvantages of m-banking . . . . . . . . . . . . . . . . . . . . . . . . 51 - 53

4.8.3Factors serving to improve the attractiveness of m-banking . . . . . . 53 - 54

4.9 Extended TAM questions & discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 - 59

4.10 Perceived importance of m-banking through identifying respondents

likelihood of switching banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 - 61

CHAPTER 5CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 - 64

CHAPTER 6LIMITATIONS & FUTURE RESEARCH . . . . . . . . . . . . . . . . . . . 65

6.1 Limitations of study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

6.2 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 - 66

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 - 74

APPENDICES (A) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Appendix 1 (A1)Review of the UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

-A1.1 Predicted drivers in the adoption of m-banking in the UK: growing

smart-phone market and improved provision of access to the Internet via a

mobile device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 - 79

-A1.2 The provision of m-banking in the UK retail banking sector . . . . . . . 79 - 84

Appendix 2 (A2)Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 - 92

Appendix 3 (A3)Chi-Square test for gender & income . . . . . . . . . . . . . . . . . . . 93 - 94


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TABLE OF FIGURES

Figure 1 - The Theory of Reasoned Action Model (TRA) . . . . . . . . . . . . . . . . . . . . . 10

Figure 2 - Original Technology Acceptance Model (TAM) . . . . . . . . . . . . . . . . . . . . 11

Figure 3 - Extended TAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 4 - Research model: an extended TAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 5 - An overview of the constructs/variables utilised in the research model . . . 22 - 23

Figure 6 - Structure of survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 - 29

Figure 7 - Breakdown of total respondents ages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Figure 8 - Breakdown of total respondents occupations . . . . . . . . . . . . . . . . . . . . . . 36

Figure 9 - Breakdown of total respondents average net monthly income . . . . . . . . . 36

Figure 10 - Total respondents frequency of channel usage . . . . . . . . . . . . . . . . . . . . . 38

Figure 11 - Total respondents awareness of provision by their bank. . . . . . . . . . . . . . 41

Figure 12 - Total respondents awareness of specific service provision by their bank 42

Figure 13 - Interest levels pertaining to specific m-banking services/functions . . . . . . 45

Figure 14 - Chi-Squared tests for significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 - 47

Figure 15 - M-banking usage advantages as identified by respondents . . . . . . . . . . . . 51

Figure 16 - M-banking usage disadvantages as identified by respondents . . . . . . . . . . 53

Figure 17 - Factors which respondents state would make m-banking more attractive 54

Figure 18 - TAM extent statements data total respondents . . . . . . . . . . . . . . . . . . . 58

Figure 19 - TAM model questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Figure 20 - Respondents likelihood of switching banks . . . . . . . . . . . . . . . . . . . . . . . 61

Appendix

Figure A1

UK smart-phone OS usage July 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Appendix

Figure A2

UK smart-phone market shares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78


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Appendix

Figure A3

Smart-phone OS market share forecast 2010 vs. 2014 . . . . . . . . . . . . . . . . 79

Appendix

Figure A4

Consumer intention to use / would switch banks if m-banking not offered 80

Appendix

Figure A5

Stages of m-commerce deployment, globally . . . . . . . . . . . . . . . . . . . . . . . 81

Appendix

Figure A6

Current key players in m-banking provision in the UK: an insight into

what choices consumers have in m-banking . . . . . . . . . . . . . . . . . . . . . . . . 83 - 84

Appendix

Figure A7

Relationship between respondents income and their interest in

using/adopting m-banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94


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LIST OF ABBREVIATIONS / TERMINOLOGY

3G (Connectivity) Method of connecting to the Internet wirelessly

ATM Automated teller machine

BI Behavioural Intention

E-commerce Electronic-commerce

FS Financial services

generation X Generation of people born in the 1960s and the 1970s

generation Y Generation of people born between the late 1970s and the late 1990s( - Consumers aged 18-34 for this study (Forrester Research, 2010))

IS Information system(s)

IT Information technology

M-banking Mobile-banking

M-commerce Mobile-commerce

OS Operating system (Software run on a computerised device)

PDA Personal-digital-assistant (type of mobile device)

TRA Theory of Reasoned Action

SMS Short-messaging-service (text message)

SST Self-service-technology

TAM Technology Acceptance Model

WAP Wireless-application-protocol


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CHAPTER 1INTRODUCTION

1.1 Background

The financial services (FS) industry has seen extensive operational changes over the past

few decades. Keeton (2001) noted that it is evident that the industry has been undergoing a

profound transformation, and this is still relevant today. Increased changes in the retail banking

environment (internal and external), heightened competition from new players, globalisation,

product innovations and technological advancements have served to substantially shape the

sector as we know it today and have created a market situation in which the battle for customersis intense. Consequently, this has led retail banks to develop a wider range of innovative service

products, offered through various delivery channels in order to increase scope of provision,

increase customer satisfaction and improve operational efficiency. The delivery of multi-channel

services constitutes an important part of these efforts and it is crucial for banks to understand

how customers interact with various distribution channels. One of the most recent services to be

offered is a wireless delivery channel, facilitating access to banking services from a range of

mobile devices.

Since the late 1970s, there has been a shift away from the traditional bricks and mortar,

branch dependant provision of banking services and a shift towards self-service methods of

banking facilitated by innovations in self-service technology (SST) in the retail banking sector.

This shift has accelerated in pace over the past decade due to the evolution of banking products

and services made available via the Internet and mobile devices as distribution channels. It has

been estimated that the sector will see further change due to the further evolution of banking

services via mobile devices, known as mobile-banking (m-banking), facilitated by the growing

smart-phone market (Forrester Research, 2010). M-banking, as a product or service, is very

much in the introduction stage of its life cycle (Ennew &Waite, 2007), however has seen growth

recently due to the growing smart-phone market and ubiquity of smart-phones in addition to the

improved network connectivity, improved data speeds and numerous wireless hot-spots in recent

years, therefore making this a relatively new and novel area of research.


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1.2 M-bankinga definition

The definition of m-banking is further changing and no longer does it mean merely an

extension of a banks Internet based banking website to a smaller handset (Banks, 2010).

Smart-phones like the iPhone and BlackBerry, facilitate mobile applications and offer high levels

of functionality but with a small screen, providing mobility and spatial independence when using

m-banking (Lewis et al., 2010).

The scope of m-banking is extending rapidly to encompass many types of financial

products and services and therefore boundaries for definitions are becoming increasingly blurred

(Wilcox, 2009a). M-banking can be defined as the provision of banking services to customers

on their mobile devices (Wilcox, 2009a), and more specifically as the manipulation of a (bank)

current, deposit or savings account (Donner & Tellez, 2008). The increasing range of devices

that facilitate access to m-banking via the mobile Internet has served to further blur the definition

of m-banking. Mobile/cell phones, smart-phones, personal digital assistants (PDAs) and even

highly innovative music players and portable games consoles, such as the iPod Touch, can

connect to the mobile Internet. This therefore means these devices can access the same m-

banking services as a smart-phone (with the exception of being able to send and receive text

messages), meaning that it is not just a consumer with a mobile-phone that can access m-

banking. For the purpose of this dissertation, in order to achieve a level of specificity, the focus

will be on banking through mobile/cell phones and smart-phones (i.e. devices which you can

make phone calls from, and which you pay a monthly tariff for or pay-as-you-go).

Retail banks offering m-banking as a channel of distribution can allow customers access

to their accounts in a variety of ways, and services offered generally fall under two categories:

informational (account balance, list of recent transactions etc.), and transactional (transfer funds

between accounts, make a payment to a person (peer-to-peer, P2P), pay a bill etc.)). The method

of account management, and platform which this service is offered on generally depends on the

type of handsets that are prevalent in the region the bank serves, in addition to the demographics

the bank is targeting. Juniper Research (2010) state that m-banking, from a technology

viewpoint, can be delivered in three main ways (on three main platforms):

- Message-based (SMS)- Mobile Internet browser (WAP browser)- Downloadable application (typically Java or other smart-phones)


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1.3 Rationale & significance of study

M-banking via SMS is becoming a popular distribution method amongst banks, and is a

good way to improve customer service and satisfaction. M-banking via the mobile Internet is a

relatively new innovation in the retail banking sector, which is constantly evolving,

encompassing different features and becoming increasingly available on more devices (Wilcox,

2009a). King (2010) identifies that there a number of benefits for banks in employing m-banking

as part of a distribution strategy: low costs compared to branches and call centres; increased

customer acquisition and strengthening customer loyalty (especially amongst generation Y);

sales and leads (targeted marketing, for example, showing customers where the nearest branch is

located); strategic value (SMS alerts versus postal notifications); and cross-selling. Clearly there

are benefits to be reaped from employing m-banking in the distribution of FS however, King

(2010) further discusses that banks have concerns regarding m-banking offerings: technological

implementation at a time of operational cost-cutting; the fast moving and dynamic mobile phone

market (concerns regarding what the next big thing will be); and concerns regarding how to get

it right in terms of integration, user experience, flexibility and features. Retail banks must be

convinced of consumer perceptions and demands relative to m-banking. The key drivers of

adoption in addition to the key barriers to adoption of m-banking must be identified in order to

offer retail banks a sound decision base from which to formulate strategic decisions. Consumer

perceptions concerning m-banking can considerably affect their adoption intentions regarding

the service, and in order for retail banks to successfully and profitably offer m-banking, they

must identify how consumers perceive m-banking. With the smart-phone market in the UK

growing considerably, and mobile applications for smart-phones being a relatively new

innovation, this makes this an interesting time to study the adoption of m-banking, as consumer

attitudes could differ from that of previous studies, where smart-phones wont have had such a

high penetration and adoption rate.

1.4 Structure of the dissertation

The relevant literature is reviewed initially in the second chapter, which provides a

background to the research being conducted in this study. The research gap is identified and

discussed in the third chapter in addition to the methodology employed, which involves

discussing the survey employed in data collection. The fourth chapter entails data analysis,interpretation and discussion of findings. The findings from the survey are analysed here and


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discussed, and a number of recommendations are made for retail banks regarding m-banking

employment in their distribution strategy. The fifth chapter concludes and summarises the key

findings, followed by a chapter highlighting the key limitations of this study, and future research

guidance pertaining to the adoption of m-banking.


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CHAPTER 2LITERATURE REVIEW

Introducing a new product or service into the market cannot be achieved without having

to consider an array of potential hazards. According to Foxall (1984), the majority of new

product and service innovations fail and at a considerable cost to the companies introducing

them. Before releasing in a market, a company should therefore assess the market in which they

are operatingidentifying the market conditions and characteristics belonging to the consumers

they are targeting are two key elements of this assessment. The characteristics belonging to these

potential adopters must be identified in order for banks to tailor their m-banking service

according to how they predict the consumer will interact with this channel of distribution, andadditionally gauge their target market. Investments should be made in attempting to better

predict consumer perceptions of the new products and/or services and how likely these are to

gain market acceptance. Market analysts at Forrester (2010) have stated that m-banking

provision is gathering pace in the UK and have predicted that m-banking will displace Internet

based banking for routine interactions in the future. If retail banks are to remain competitive in

the UK market, and reap the benefits ofm-banking, they must first identify how this service is

perceived by potential adopters, and identify what services consumers deem as necessary to

access via a mobile devicethis will provide banks with an insight into what consumers expect.

There is an increasing interest in researching the adoption of m-banking however,

literature on the subject is very much in its infancy (Hernandez et al., 2010). The recent growth

in the smart-phone market has implications as to the validity of findings in previous research

papers as the smart-phone provides a platform upon which banking services can be accessed.

The growth in popularity of the smart-phone therefore serves as a factor which could potentially

facilitate the adoption of m-banking due to consumers being better equipped to access services

via high speed mobile Internet, and also altered consumer perceptions of m-banking, as they may

relate this service to social status. This alteration in the market environment could potentially

change consumers perceptions of m-banking for example, consumers may now perceive m-

banking to be more convenient if they have a phone which facilitates high-speed access.

Customer perceptions, and the key drivers and inhibitors of m-banking adoption, must be

identified providing retail banks with a sound base on which strategic decisions can be made, in

order to maximise the potential for gaining custom via this new self-service channel.


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2.1 Structure of the chapter

The core goal of this chapter is to explore the relevant literature in the field of m-banking

and identify the key motivators and inhibitors for the consumer adoption of m-banking,

particularly those that affect the consumer's attitude towards, and intention to use, this self-

service banking technology.

The chapter begins with an overview of the trends in self-service-technology (SST) in the

retail banking sector, which have served to shape the industry as we know it today. Previous

research conducted concerning the consumer perceptions and attitudes influencing the adoption

of electronic methods of banking will also be outlined thus, both providing a historical context

for this study. The significance of the fast growing smart-phone market will be discussed, in

addition to an assessment of the current level of m-banking provision in the UK retail banking

sector, outlining the consequences this has for m-banking adoption amongst the lucrative

generation Y this will provide the contemporary context in which this study is situated and

will highlight the significance, and therefore justification as to the relevance of this study. The

core goal of this section of the literature review is to identify the market conditions and assess

whether they are driving or inhibiting m-banking adoption amongst generation Y consumers in

the UK retail banking market.

Literature by various authors concerning the usage and adoption of this new channel of

banking will be explored. Previous research conducted on the consumer adoption of electronic

channels in banking will be considered. Two concepts which have been used by authors to better

predict the adoption of technology systems are reviewed here, and will be employed together to

form one research model in the next chapter providing a theoretical framework for the study of

m-banking; the Technology Acceptance Model (TAM) and the Theory of Reasoned Action

(TRA). Common trends identified in the literature will be grouped together thematically, and the

research model discussed.

2.2 A shift away from bricks and mortar: an overview of literature pertaining to the

adoption of electronic channels of banking

The FS sector has seen considerable change over the past few decades and it is importantto understand how these changes have arisen, as ultimately they have led to a shape the current


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market, in which there is a greater than expected market demand for m-banking services in the

UK (Mansfield, 2010). A shift towards the self-service provision of financial services (FS)and

away from the branch dependant provision of FS has been assisted by innovations in self-

service channels of distribution in the sector in addition to the acceptance of these electronic

methods of banking. Electronic-banking is seen as one of the most successful business-to-

consumer applications in electronic commerce (Pousttchi & Schurig, 2004), and there has been a

mass of research conducted into consumer perceptions and the ways in which consumers interact

with these channels of distribution. By considering the literature pertaining to the adoption of

other self-service, electronic channels of distribution, an insight will be gained into how

consumers may perceive m-banking, as many of the benefits will be transferrable between

channels, such as convenience.

The trend towards self-service in the retail banking sector was initialised by the advent

and evolution of the automated teller machine (ATM) and a significant amount of research has

been conducted on consumer attitudes towards this e-banking platform: Rugimbana, 1995;

Filotto et al., 1997; Moutinho & Smith, 2000; Littler & Melanthiou, 2006. The introduction of

telephone based banking (tele-banking) saw a further move away from the branch and towards

the creation of the branch independent consumer, and studies have the consumer adoption of

tele-banking: Lockett & Littler, 1997; Al-Ashban & Burney, 2001. Over the past decade, the

retail banking sector has seen an accelerated pace of change due to the introduction and

mainstream consumer acceptance of the Internet as we know it today, and the subsequent

introduction and evolution of Internet based banking. The substantial consumer acceptance of

Internet banking in the UK has led to the biggest operational change the sector has seen (Sayar &

Wolfe, 2007). Significant changes were made to the ways in which customers consumed FS, and

allowed for banks to considerably cut their operating costsresearch has revealed that banking

via the Internet is the cheapest distribution channel for many banking services and this cost

saving has been passed on to the consumer making this an increasingly popular method of

banking (Robinson, 2009; Ho & Lin, 2010).

Literature existing on the consumer perceptions of Internet banking and the adoption of

this channel is substantial (Lee et al. 2009). Additionally, and more specifically, there has been

an interest in studying the perceptions and attitudes towards this channel of distribution by the

young consumer. Calisir and Gumusoy (2008), and Chau and Ngai (2010) found that uptake of


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electronic methods of banking has been particularly great amongst the young consumer,

justifying the significance of this demographic to retail banks regarding m-banking.

Considering electronic methods of banking, other than m-banking, Karjaluoto et al.

(2002a) identified cost and time savings plus twenty-four-seven provision as the most beneficial

elements of Internet banking, and Howcroft et al. (2002) found spatial independence to be a key

benefit of adopting Internet banking over other channels. Ease-of-use (Karjaluoto et al., 2002),

speed of service delivery (Karjaluoto et al., 2002), and convenience and compatibility with

lifestyle (Black et al., 2002; Gerrard & Cunningham, 2003) are additional factors which have

contributed to the usage of this channel versus the branch. Complexity of service (Blacket al.,

2002), perceived financial cost (Black et al., 2002), ignorance of electronic services (Sathye,

1999) and security and privacy concerns (Sathye, 1999; Cheung & Liao, 2001; Black et al.,

2002; Lee et al., 2009) were found to inhibit the usage of Internet banking, with consumers

identifying these factors as significant. However, the findings of Karjaluoto et al. (2002a) and

Littler & Melanthiou (2006) yielded findings indicating that security and privacy concerns are

not the most significant factors in hindering the adoption of Internet banking, which conflicts

with previous studies from Sathye (1999), Cheung & Liao (2001), Blacket al. (2002) and Lee et

al. (2009), amongst others. Many factors identified as serving to drive, and conversely inhibit the

adoption of Internet banking, will be applicable in many contexts to m-banking as a channel of

distribution as m-banking can be seen, in many ways, as an extension of Internet banking to a

smaller device (Lewis et al., 2010; Butcher, 2010).

The mobile phone, as a channel for service consumption, offers enormous potential since

the device is increasingly becoming an integral part of customers lives (Laukkanen, 2007).

Consequently, there is an increasing interest in researching the adoption of m-banking (Lee et

al., 2009). Earlier studies (perhaps now considered historical in the m-banking context)

indicate that factors contributing to the adoption of m-banking are related to convenience, access

to the service regardless of time and place (ubiquity), privacy and savings in time and effort

(Suoranta, 2003). There are however factors which have been identified as serving to inhibit the

use of mobile channels in banking transactions. Previous studies, conducted a few years ago,

indicate that perceived financial cost (Luarn & Lin, 2005) and perceived complexity (Lee et al.,

2003) served to inhibit the use and adoption of m-banking services. Furthermore, security issues

are argued to be among the greatest concerns in the adoption of m-banking (Brown et al., 2003;Luarn & Lin, 2005). Conflicting with previous findings, certain authors have argued, based on


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their findings, that security issues are not perceived by customers to be major obstacles in

banking transactions (Suoranta, 2003; Laukkanen & Lauronen, 2005). These studies state that m-

banking was found a secure way to conduct banking transactions by the users.

2.3 Review of the UK

In order to accurately investigate the drivers and inhibitors of m-banking in the UK retail

banking sector amongst generation Y consumers and measure these consumers attitudes

towards, and intention to utilise, this self-service channel of distributionit is crucial to appraise

the current market environment in which m-banking is situated. An overview of the growing

smart-phone market and improved mobile Internet connectivity (which both serve as facilitators

in the potential for heightened m-banking adoption amongst the lucrative generation Y) will be

provided initially in Appendix 1 (A1.1). This will be followed by an appraisal of the current

market situation, and level of m-banking provision in the UK retail banking sector, inAppendix 1

(A1.2).

2.4 Theoretical framework

Previous studies have suggested the use of some theories and frameworks for application

in the m-banking context, however there is no standard on how to apply these theories. From a

review of the literature, the researcher has identified two common models used in the

explanation of innovation usage in an electronic-banking context: the Technology Acceptance

Model (TAM); and the Theory of Reasoned Action (TRA).

2.4.1 Technology Acceptance Model (TAM)

A variety of models have been suggested to explain innovation usage, however, the

TAM, as proposed by Davis et al. (1989), has evolved as the most popular model (Luarn & Lin,

2005). The TAM is an adaption of the Fishbein & Ajzen (1975) TRA model, which claims that

behaviour is a direct consequence of behavioural intention (BI). The TRA model, as illustrated in

Figure 1, is a well established and influential theory which has been used in a broad range of

studies in the determination of human behaviour in varying contexts (Vekantesh et al., 2003).

According to Fishbein and Ajzen (1975), the most important determinant of a persons behaviour

is BI, which is defined as the strength of a persons intention to perform a specified behaviour.The TRA suggests that a range of social and personal beliefs regarding a particular behaviour


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determines one's intention to perform, or not perform, a particular behaviour. It suggests that

ones intention to perform a particular behaviour is a combination of the attitude towards

performing the behaviour and the persons subjective norm, which is an individuals perception

of social normative pressures, or relevant others beliefs that he or she should or should not

perform such behaviour (Fishbein & Ajzen, 1975). Thus, it will be BI, rather than attitudes, that

will determine actual behaviour. The TRA is more of a generalised model than the TAM, and

can be used in a broader spectrum of contexts explaining behaviour beyond just the adoption of

technology, in contrast to the TAM which is only applicable to technological contexts.

Figure 1: The Theory of Reasoned Action Model (TRA)

Source: Fishbein & Ajzen (1975)


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Davis et al. (1989) defined the TAM, shown in Figure 2, as having two basic

determinants perceived usefulness and perceived ease of use which are said to be

instrumental in the explanation of users intention and behaviour towards the use of new

technology. According to the TAM, BI is influenced by the users attitudes towards a system.

The users attitude towards a system is said to be affected by the perceived usefulness of the

system, in addition to the systems perceived ease of use (Davis et al., 1989). The intention to

utilise a system plays a key role in the TAM. Intentions reflect the motivational factors that

affect user behaviour for example, the amount of effort a user will expend in attempting to

perform a particular behaviour (Ajzen, 1991). Ajzen (1991) stated that the stronger the intention

of the user to engage in a particular behaviour, the more likely that user will actually engage in

the behaviour.

Figure 2Original Technology Acceptance Model (TAM)

Source: Davis et al. (1989)

Perceived usefulness refers to the degree to which using a specific information system

(IS) will increase a users job performance (Davis et al., 1989), or if defined in a more

universally applicable sense, perceived usefulness is the extent to which a person views aninnovation as having an advantage over a previous method of performing the same task (Taylor

& Todd, 1995). Perceived ease of use refers to the extent to which the use of the system is free

from effort (Davis et al., 1989). Koufaris (2002) found that the perceived usefulness of a virtual

store was positively related to the intention to use this virtual store again. Ease of use is a good

predictor for the usage of m-commerce services as Hung et al. (2003), Koivumaki et al. (2006)

and Wang et al. (2006) found that ease of use was a significant factor in determining the

acceptance of mobile services, and positively affected the intention to utilise mobile services.

Certain studies have found that usefulness is affected by convenience (Chen, 2008) in addition to


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compatibility and ease of use (Wu & Wang, 2005). In terms of m-banking, both perceived

usefulness and ease of use have been said to affect its adoption, according to findings yielded in

studies reviewed by the researcher (Luarn & Lin., 2005; Lee et al., 2009; Lewis et al., 2010

amongst others).

The TAM has been proved and tested to be a valid and relatively reliable model in the

examination of IS acceptance and use (Pikkarainen et al., 2004). However, as with many models,

the TAM has been subject to criticism. Some authors have criticised the TAM because of its

deterministic approach on the decision to adopt or reject IS (McMaster & Wastell, 2005).

However, as identified by Davis et al. (1989), future research of IS and information technology

(IT) usage has to address other variables which affect usefulness, ease of use, user acceptance,

and therefore user adoption. Davis et al. (1989) stated that these two determinants may not

conclusively explain the factors which are predictive in the acceptance of a technology

application, such as m-banking.

Prior studies have also extended the original TAM in an attempt to broaden the spectrum

of its predictive ability, with added constructs. Luarn and Lin (2005) modified the original TAM

by adding perceived credibility, which was defined by Wang et al. (2003), perceived financial

cost which was found in Mathieson et al. (2001), and perceived self-efficacy, which Luarn and

Lin (2005) state was confirmed by several prior studies.

2.5 Prior studies which have extended the TAM: encompassing additional constructs

According to Luarn and Lin (2005), the TAM has been applied in the context of mobile

financial services in several prior studies, in order to gain insight into user acceptance. Wang et

al. (2003) introduced perceived credibility as a new construct to the TAM that reflects security

and privacy concerns in the acceptance of electronic channels of banking. It was found that

perceived credibility significantly affects consumers BI to use this service. Other research also

suggests that perceived credibility positively affects users BI to use mobile services (Wang et

al., 2006) and m-banking (Luarn & Lin, 2005). Amin (2007) extended the applicability of the

TAM in a mobile payment context, suggesting that perceived usefulness, perceived ease of use

and perceived credibility are important determinants in predicting Malaysian bank customers

intentions to use a mobile payment services. Lee (2007) identified perceived risk and perceivedusefulness as key factors in influencing the adoption of m-banking. According to Suoranta and


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Mattila (2003), perceived risk is a very significant factor in the adoption of m-banking amongst

consumers. Chen et al. (2004) included perceived service quality and compatibility in their TAM

model for user acceptance of virtual stores. Kindberg et al.(2004) investigated users perception

of security in mobile interactions and observed that in a mobile technology context, the users

have to make dynamic decisions about the trustworthiness of the service provision with little or

no prior information known about the other parties in the interaction. Luarn and Lin (2005)

determined users acceptance of m-banking by adding one trust-based construct (perceived

credibility) and two resource-based constructs (perceived self-efficacy and perceived financial

cost) to the model, creating an extension of the TAM (see Figure 3). The attitude construct,

as seen in the original TAM (see Figure 2), was removed by Luarn and Lin (2005) for

simplification.

Figure 3extended TAM

Source: Luarn & Lin (2005)

The extended TAM, as defined by Luarn and Lin (2005), will be utilised in this study in

determining customers usage intentions regarding m-banking, which will provide insight into the

drivers and inhibitors of m-banking adoption amongst young consumers in the UK retail banking


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sector. The added constructs of the extended TAM will now be reviewed, followed by the

identification of other, additional constructs which have been added to Luarn and Lins TAM.

2.5.1 Self-efficacy

The proposed relationship between perceived self efficacy and perceived ease of use is

based on the theoretical argument by Davis (1989) and Mathieson (1991), according to Luarn

and Lin (2005). Based on the Theory of Planned Behaviour, Mathieson (1991) found that

perceived knowledge resources had a significant positive influence on BI to use an IS. According

to Luarn and Lin (2005), empirical evidence exists suggesting a causal link between self-efficacy

and perceived ease of use (Agarwal et al., 2000; Venkatesh et al., 2003). Luarn and Lin (2005)

highlighted that previous studies (Harrison & Rainer, 1992; Agarwal & Prasad, 1999) have

suggested a positive relationship between experience with computing technology and computer

usage. According to Luarn and Lin (2005), self-efficacy has been considered as an additional

construct to the TAM in a range of IS studies (Agarwal et al., 2000; Chau, 2001; Hong et al.,

2001; Johnson & Marakas, 2000), and is considered as critical in the understanding of individual

response to IT.

2.5.2 Security (sub-variable of credibility)

Consumer security concerns have a significant impact on the usage intentions of m-

banking amongst consumers and play a pivotal role in mobile communications (Massoud &

Gupta, 2003). Security deals with the issue of hackers gaining unauthorised access to personal

financial information and ultimately removing money from a users bank account(s) (Littler &

Melanthiou, 2006). Laukkanen (2007) found that both young and mature consumers consider the

security risk that third parties could get access to their bank accounts while using m-banking

services. Sivanand et al. (2004) found that 68 percent of respondents in their survey rated the

level, or quality, of security in conducting financial transactions via mobile networks as

unsatisfactory. However, it was found in results yielded from another study, that the majority of

respondents perceived mobile connections as relatively secure and certainly more secure than

public Internet networks (Laukkanen & Lauronen, 2005). Kim et al. (2008) found that security

was a major determinant in customers intention to purchase online, and Howcroft et al. (2002)

and White and Nteli (2004) found that UK consumers ranked security as the most important

attribute of electronic-banking service quality. Another study, conducted in Turkey, reported that

young consumers are also particularly concerned about security issues (Calisir & Gumussoy,


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2008). Similarly, Laforet and Li (2005) found the issue of security to be the most important

factor that motivated Chinese consumers to adopt m-banking.

2.5.3 Privacy (sub-variable of credibility)

According to Aldas-Manzano et al. (2009), consumer disappointment and annoyance

regarding violations of consumer privacy was another significant barrier to the adoption of e-

commerce. Featherman & Pavlou (2003) found that certain consumers were worried about the

misuse or theft of their private information when utilising electronic services. Similarly, Gerrard

& Cunningham (2003) found that consumers were concerned that financial institutions may

monitor the types and frequencies of services they employ and then try to cross-sell additional

products based on the consumers identified usage of various channels and service preferences.

Consequently, online banking customers have identified they are reluctant to enter personal

information when websites ask for it (Roca et al., 2009). Some empirical studies have suggested

that perceived privacy is a crucial factor in customers acceptance of electronic services

(Howcroft et al., 2002; Luarn & Lin, 2005; Lee et al., 2009). Consequently, privacy is

determined as being a key factor which can inhibit the usage of online banking and m-banking

(Lewis et al., 2010). In the m-banking context, Chen (2008) found that users had fears about

privacy when using m-payment and highlighted concerns about personal data being intercepted

or accessed by unauthorised third parties. In the same study by Chen (2008), respondents thought

that companies may potentially store private information about them in an inappropriate way and

may use if for improper purposes. Amin (2008) found, in a study about mobile phone payment,

that customers will only use mobile phone payment methods if the mobile system could protect

their privacy.

2.5.4 Financial cost

Perceived costs which include purchase costs and switching costs should be taken into

consideration (Lewis et al., 2010), as these costs will be an essential influence on the decision of

whether to use m-commerce (Hung et al., 2003; Wu & Wang, 2005). Gressgard and Stensaker

(2006) suggest that switching costs can be very high for customers due to their technological

uncertainty about particularly new and innovative products or services. According to the

consumer behaviour literature, high purchase or switching costs can ultimately lead to greater

resistance and thus slow diffusion rate (Hoyer & MacInnis, 2007). In general, switching costs

encompass all factors that hinder customers from switching to other service providers. Forexample, loyalty points or membership card schemes play important roles in the mobile


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telecommunications industry (Ahn et al., 2006). Wu and Wang (2005) found that perceived

financial cost has a significant negative effect on users BI to use m-commerce. Perceived

financial cost was also found to be a significant factor influencing the BI to use an IS (Mathieson

et al., 2001). Sathye (1999) found that cheaper costs, perhaps achieved through lowering

overheads by having no tangible branch, can motivate consumers to use electronic channels of

banking. Luarn and Lin (2005), in a qualitative aspect of their research, found several consumers

that identified financial cost considerations as influencing their BI to use m-banking.

2.5.5 Adding constructs to Luarn & Lins extended TAM

Perceived risk is a construct taken from Yu (2009) and additionally, Lewis et al. (2010).

In the research model to be used by the researcher, perceived risk was altered to perceived

overall risk in order to gauge, in a general sense, how risky consumers perceive using m-banking

to be risky. Lewis et al. (2010) state that risk is a multidimensional concept, and identified six

types of perceived risk in the literature. Lewis et al., (2010) identified, from an appraisal of

relevant literature that Greatorex and Mitchell (1994), and Stone and Gronhaug (1993) identified

performance, financial, physical, social, psychological, and time risk as being differing

variations of a consumers perceived risk. Lewis et al. (2010) highlighted that Zhao et al., (2008)

found that customers have difficulty in assessing and differentiating the various risk dimensions

meaningfully, especially if they had not much experience of m-banking services. Lewis et al.

(2010) found support to this statement by Wolfinbarger and Gilly (2003), who found that

consumers may find it difficult to evaluate the financial risk related to online or m-banking. For

this reason, the researcher has deemed that an in-depth look into consumers perceptions

regarding risk is out of the scope for this research. Hence, overall perceived risk has been used

as a catch-all variable which will be used to determine respondents perception of the overall

risk using m-banking has. Risk is linked to perceived credibility as many consumers associate

this with security and/or privacy issues.

Lewis et al. (2010) identified that compatibility has been added to the TAM in prior

studies: virtual store (Chen et al., 2002); m-payment (Chen, 2008); m-commerce (Wu & Wang,

2005). The perceived compatibility variable was used by Lewis et al., (2010) in the m-banking

context. Compatibility is an important aspect of innovation that can be defined as the extent to

which a new service is consistent with users existing values, beliefs, previous experiences and

habits (Chen et al., 2002). Rogers (1995) stated that innovations compatible with an individualusers lifestyle will result in a faster rate of adoption. According to Lewis et al. (2010) research


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from prior studies (Agarwal & Karahanna., 1998; Wu & Wang, 2005) has shown that

compatibility will lead to higher perceived ease of use as less effort is required.

Perceived speed and perceived mobility are elements of customer service (both are said

to impact overall perceived usefulness of the service), and are from an extended TAM also from

Yu (2009). Chen et al. (2004) also held that perceived service quality was important in

determining BI towards adoption of innovations, and Karjaluoto (2002) specified on the service

quality stating that speed was detrimental in consumers decision to adopt technology. Both

variables were found to positively affect intention to use m-banking, and have been added to the

TAM in order to extend the scope of research. Lai and Li (2005) added gender, age and IT

competency to the TAM, suggesting they impact all the TAMs variables which affect BI to

adopt Internet banking. Further external variables have been added by the researcher for this

study which includes mobile competency and Internet banking competency, which the

researcher associates with all the variables in the TAM similar to Lai and Li (2005). The

researcher has linked IT competency, mobile competency and Internet banking competency

specifically to the self-efficacy variable as the extent to which a consumer believes they can

use m-banking will be associated with their experience with using computer systems, their

experience with using mobile devices and their experience with electronic-banking methods such

as Internet banking. IT competency and electronic-banking competency will be assumed from

the respondents usage of Internet banking. Mobile competency will be assumed from the

respondents usage of smart-phones.

2.5.6 Research model creation

It is hoped that the application of the above mentioned constructs in the TAM will enrich the

current level of quality of knowledge on m-banking adoption. By re-applying some of the already

used TAM constructs identified from other studies, it is hoped that additional insight will be gained.

These constructs play a valuable part in the creation of research questions. By adding new variables

to the TAM, it is hoped that a different insight will be gained into the key drivers and inhibitors of m-

banking adoption. To summarise, the perceived risk construct has been generalised to perceived

overall risk, perceived credibility has been split into perceived security and perceived privacy for this

research model, in order to highlight the importance of the sub-variables, electronic-banking

competency and mobile competency have been introduced and linked directly to perceived self

efficacy, although are said to potentially serve to effect all variables in the TAM model.


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CHAPTER 3RESEARCH GAP & METHODOLOGY

3.1 Structure of the chapter

This chapter of the dissertation begins with highlighting the research gap which was

identified through an appraisal of the relevant literature. This was to create a unique study which

avoided the repetition of work of others. Additionally, an appraisal of the literature provided a

degree of guidance regarding the constructs/variables to be employed in the research and in the

creation of research questions. It is hoped the specificity of the study, will enrich the current

level of knowledge and understanding regarding key barriers in relation to m-banking adoption.The research model to be employed in the research will be outlined the constructs of the

research model are based on the extended TAM as defined by Luarn and Lin (2005), in addition

to findings from empirical studies of others as identified in the literature review. The research

design will be discussed, highlighting the key advantages and disadvantages of employing a

questionnaire in data collection versus other methods providing justification of the selection. The

online survey design will be outlined and a specific structure of the final survey to be used will

be provided in tabular form. A broad overview of the data collection procedure will be provided

which entails an identification of the target population, sampling methods used in the study, in

addition to the administering of the online survey. The data preparation and analysis will then be

considered, outlining the methods to be utilised in the next chapter.

3.2 Research gap

The discussion of the research gap will encompass the justification and rationale for this

study, in addition to the research objective and research problem. After a review of the relevant

literature, the researcher has found a wealth of literature dealing with the adoption of online

banking and potential barriers in relation to its adoption. However, it has been observed that

literature pertaining to the adoption of m-banking is in its infancy, and still a relatively recent

topic which would benefit from further research. The researcher would not describe the list of

existing literature on m-banking adoption as being extensive, nor exhaustive in application

amongst different countries and amongst different consumer demographics, highlighting an

opportunity for further, more specific research.


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In order for a new product to succeed in the marketplace, it is necessary to know what

factors influence its adoption. From the review of the relevant literature, it was found that

various factors can affect the adoption intentions of consumers towards m-banking. It was also

found that young consumers are more likely to adopt innovations in the market and are also said

to be the group of consumers most interested in the adoption and utilisation of m-banking,

justifying the selection of this interesting and potentially lucrative group of consumers as the

main focus for the research.

A review of some common themes in the relevant literature justifies basing the research

model on the extended TAM by Luarn and Lin (2005). The themes such as perceived self-

efficacy, perceived financial cost and perceived credibility (which encompasses both security

and privacy issues) were identified in the relevant literature and are therefore deemed by the

researcher as being significant in the determination of adoption intentions towards m-banking, in

addition to the original constructs of the TAM. As mentioned, perceived compatibility, perceived

speed and mobility (customer service), perceived overall risk, age, gender, IT competency,

electronic-banking competency and mobile competency have also been added to the model.

As far as the researcher is aware, there are no studies which utilise an extended TAM in

the investigation of drivers and inhibitors of m-banking adoption in the UK retail banking sector

by considering the perceptions of generation Y consumers. The separation of the younger

consumer demographic for close study in this research is important, as this demographic is

valuable to retail banks.

The recent growth in the smart-phone market in the UK (and globally) has impacted the

market environment in which retail banks operate, potentially serving to positively affect the

adoption intention of consumers towards m-banking. The researcher has found no academic

literature which dealt with the relationship between consumers owning a smart-phone and their

intention to adopt m-banking, justifying this element of the research. In addition, no literature

was found which considered whether or not a consumers retail bank providing m-banking

served to hinder that consumers intention to utilise m-banking, justifying this element of the

research. It will be considered whether consumers may be willing to switch banks in order to be

provided with this service, which will provide an indication as to how important bank customers

perceive m-banking to be. If banks do not offer m-banking, consumers may view their bank as alaggard. This perception may carry across to other products and services provided by the bank


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and therefore negatively affect customer retention and customer acquisition, in addition to cross

sales efforts. This study therefore identifies if generation Y consumers in the UK would be

willing to switch banks if their bank did not encompass m-banking into their distribution

portfolio, and whether or not they would view their bank negatively if their bank had no

intention of investing in m-banking.

3.3 Proposed research model

The research model for this study is shown in Figure 4, and an overview of the key

constructs featured in this model are provided in Figure 5. This research model encompasses

some of the key themes which have arisen in the literature which has been reviewed by the

researcher, hence why the extended TAM, as defined by Luarn and Lin (2005), has been used as

the basis for the research model for this study. The model includes several key determinants of

technology adoption, as indicated by the TAM, the TRA and empirical findings reported in the

relevant literature which support these as being credible and significant constructs.


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Figure 4Research model: an extended TAM

Source: the researcher adapted this model from Davis et al. (1989), Wang et al. (2003), Luarn & Lin (2005), Lai & Li (2005), Yu (2009) and Lewis et al. (2010)


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Figure 5An overview of the constructs/variables utilised in the research model


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3.4 Research design

Qualitative research and quantitative research are the two broad research methodologies

which exist. Some authors view these methodologies as significantly different; others however

do not consider the differentiation important (Bryman & Bell, 2007). Both research methods can

be seen as broad categories which are applicable to an array of different studies and are often

combined in many research efforts (Hanson & Grimmer, 2007). Bryman and Bell (2007) define

quantitative research as a research strategy that emphasises quantification in the collection a nd

analysis of data whereas qualitative research is defined by Bryman and Bell (2007) as a

research strategy that usually emphasises words rather than quantification in the collection and

analysis of data. Quantitative research methods are generally considered explicit, rigorous and

easy to replicate, whereas qualitative research is linked to interpretation (Hanson & Grimmer,

2007). In this dissertation, the mass collection and analysis of data is of paramount importance

in order to identify generationY consumer perceptions and adoption intentions regarding m-

banking. Ultimately, based on the large volume responses, predictions will be formulated about

the entire population based on the sample. Hence, the quantitative questionnaire, or survey seems

an appropriate means for data collection in this study.

3.4.1 Advantages of employing a questionnaire in data collection

Questionnaires are characterised as being cheaper and quicker to administer than other

methods of quantitative research such as structured interviews as no interviewer has to be

paid and the time expended in the process is less than other methods (Bryman & Bell, 2007). The

questionnaire is therefore an efficient method for collecting data (if relevant to the nature of

feedback required by the researcher). For structured interviews, in a scenario where the

researcher themselves are conducting the interview, time is expended by the researcher on the

interview processes, of which there may be several, meaning this research method can be time

consuming. This could involve time being underutilised (travelling for instance), perhaps

resulting in a lack of productivity (Bryman & Bell, 2007), reinforcing the fact that questionnaires

hold the advantage of being more efficient over other research methods. Questionnaires are more

convenient for respondents as it gives them flexibility regarding when they complete the survey,

allows for anonymity and moreover, allows respondents to take as much time (within reason) to

complete the questionnaire (Bryman & Bell, 2007). Responses are easier influenced in an

interview, when compared with a questionnaire, as an interviewer could perhaps influenceanswers by the way questions are phrased, or the way in which questions are asked, potentially


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leading to unrepresentative answers being provided. However, it has to be considered that the

ways questions are worded in a questionnaire can influence the outcome of the answer also

(Bryman & Bell, 2007).

3.4.2 Disadvantages of employing a questionnaire in data collection

In general, a questionnaire survey method lacks in-depth research results as opposed to

more in-depth, qualitative methods. However, in the case of this study, breadth was of more

relevance and importance since the aim was to obtain a large volume of findings from which

conclusions and generalisations could be made. When implementing a questionnaire in data

collection it has to be considered that there is no interviewer, or other person, to assist

respondents if they do not understand a question. This problem could be reduced however by

piloting the questionnaire amongst a subset of the target population and ensuring the

questionnaire is intuitive and the content makes sense and flows. An interviewer being present

means they are able to probe respondents to elaborate on a particular response if they feel

information is being withheld, or the answer could benefit from further explanation or

justification more so when open-ended questions are used. Another issue existing with a

questionnaire is that the respondent could read it as a whole, hence providing respondents with

an idea, or spoiler, of what is being asked in later questions (Bryman & Bell, 2007). Respondents

might answer questions in an unintended, or illogical, order which could serve to affect the

validity of responses. Additionally, researchers do not know who actually answers the

questionnaire. It could be a family member, a colleague or a friend who answer for instance. The

researcher believes the potential for this problem has been lessened as online questionnaires were

sent out via Facebook meaning that the person needs to log-in to view the questionnaire,

reducing the chances of another person answering. Bryman and Bell (2007), state that

questionnaires entail a greater risk of having missing or incomplete data or data sets, as no one

can check all the questions have been answered. However, the researcher configured the online

survey in a way such that all questions were compulsory, meaning the survey could not be

completed and published if an unanswered question existed.

3.5 Online survey design

The researcher has deemed the quantitative online questionnaire to be the most efficient

and appropriate method for data collection in this study, as there is a significant potential forhigh volume responses, which are crucial to this study. If administered correctly via the


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appropriate channels this method could collect a high volume of relevant data, more so than

another method. In addition, it is possible to span a larger geographical area by employing a

survey as opposed to another quantitative method, such as a structured interview for instance.

Also, through a review of prior studies on m-banking, Internet banking and electronic-commerce

in general, it was found that the majority of studies used surveys in the collection of data for

analysis. This therefore justifies the choice of employing a survey in the identification of

consumer attitudes towards m-banking and consumer intentions to utilise this new channel for

consumption of FS. Figure 6outlines the structure of the survey.

The online questionnaire commenced with a short introduction, which defined mobile-

banking and informed participants of the research purpose and nature of the study. Hanson and

Grimmer (2007) suggest the more information participants receive about the content of a survey,

the higher the response rate will be. In addition all respondents were guaranteed that their

responses were anonymous. So as to encourage responses the survey was kept simple as Hanson

and Grimmer (2007) state that in order to maximise responses, questionnaires and surveys

should not be complex. The survey was guaranteed to take less than 5 minutes, with the average

time taken by respondents during the pilot of the survey being just over 4 minutes. The key

rationale behind keeping the survey short was in order to achieve maximum responses, as Sellitto

(2006) holds that a voluntary survey that is kept short and concise is conducive to achieving a

relatively higher response rate.

Simple questions were placed strategically at the start of the survey as difficult questions

at the beginning may serve to deter respondents from completing the survey (Sellitto, 2006).

Additionally, questions were placed in an order so as to arouse interest in the topic. Closed-

ended questions, as opposed to open-ended questions were used in the most part throughout in

order to aid respondents in completing the survey in a time efficient manner and allow decision

time to be shortened. Cavana et al. (2001) state this is important in surveys as it allows greater

uniformity, thereby making the analysis of data simpler. Surveys are useful in the discovering of

both facts and opinions such as attitudes (Denscombe, 1998) therefore, attitudinal, Likert scales

were employed in certain questions in order to maximise the specificity and usefulness of the

data received. Likert scales can be described as comprising of three or more ordinal (ranked)

scale categories that are positioned along a continuum (Busch, 1993), and often have a neutrality

option, such as neither/nor. In prior studies, Likert scales have been used more frequently witha five-point or seven-point format, which is conducive to ease of use (Preston & Colman, 2000).


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Fishbein & Ajzen (1975) hold that beliefs and attitudes are best measured by means of Likert

scales and according to Busch (1993) scale length should be based on the concept being rated

and the participants familiarity with this idea. Thus, Busch (1993) states that questions

regarding unfamiliar topics should comprise a smaller number of scale categories.

The survey was presented on one page so as the respondents could scroll down and view

how many questions there were. In addition there was a progress bar at the foot of the page

allowing respondents to view the percentage of the survey they had completed. Both these

methods were employed so as to provide the participants with transparency by allowing them to

view the survey as an entire entity and not break it up on several pages. It was hoped this would

encourage respondents to complete the survey after opening the link. An overview of the

structure of the survey is provided in Figure 6, and the final survey, used in the collection of

data, is presented inAppendix 2 (A2).


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Figure 6Structure of survey


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3.6 Data collection procedure

It would be inconceivable to administer a survey to the entire population of the UK due

to cost and time constraints, amongst a lack of other resources, therefore, it is necessary to

survey a sample of the population and formulate predictions about the entire population based on

that sample. In positivist studies, selecting a sample and sample size are both important stages of

the research. Hussey and Hussey (1997) state that a sample size should be unbiased and large

enough to satisfy the needs of the research purpose. For this study, all consumers in the UK who

belong to the generation Y demographici.e. 18 to 34are part of the target population, and

from that, a sample must be selected. Bryman and Bell (2007) define a population as any

complete group of entities that share some common set of characteristics and define a sample as

a subset of larger population. If a sample of the target population is used, costs for the research

will be kept at a minimum and as a result the data collection period can be shorted considerably

as it would take less time to obtain data from a subset of population versus the entire population.

3.6.1 Sampling methods employed

Bryman and Bell (2007) identify two main sampling methods to choose from when

conducting quantitative research: probability and non-probability sampling. With probability

sampling every member of the population has a known, non-zero probability of selection and

consequently are selected at random, whereas with non-probability sampling, the probability of

any member of the population being chosen is unknown (Bryman & Bell (2007). An advantage

of probability sampling is that results can be generalised to the entire population meaning the

sample would be representative of the target population however; achieving a true probability

sample can be costly in terms of finances, time and human resources (Bryman & Bell, 2007).

Convenience sampling, quota sampling and snowball sampling are all types of non-probability

sampling methods, and will be employed together in this study.

Convenience sampling involves the researcher approaching people relevant to the study

who are easily accessible, therefore convenient to access. Quota sampling methods produce a

sample that aims to reflect the target population with regards to specific characteristics such as

gender or agefor this study; the researcher has contacted people who are UK residents who are

between the ages of 18 and 34. Based on the initial contact established with those people a

snowball sampling method has been employed to gain further relevant responses to the survey.Snowball sampling can be seen as a type of convenience sample as it involves the researcher


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approaching people who are accessible and relevant, who then establish contact with more

people who are accessible and relevant to the study and consequently the data sample pool grows

bigger and bigger. For this study the researcher approached 50 of his online Facebook friends

and asked them to complete the survey, and additionally forward the link to the survey on to 10

of their friends who were between the ages of 18 to 34 years old and were UK residents. The

sample size was therefore 500, assuming all the Facebook friends committed to the agreement.

The researcher aimed to achieve at least 150 responses and as a contingency plan, also posted the

link to the survey on his Facebook page, inviting users to complete the survey, with the hope this

would achieve a high volume of responses this rendered it impossible to identify how many

people responded to the survey via this link, or via the snowball method. It was hoped that the

snowball sampling strategy would increase the likelihood of participating in the survey as all

receivers know the sender of the message.

3.6.2 Administering the online survey

For this study, an online survey was created using a website called Surveymonkey.

Online surveys allow the evaluation of quantitative responses to questions and involve low costs

in general (Wilson & Laskey, 2003). Luo (2009) identified that the majority of Internet users are

young and stated this was a potential problem as the sample of respondents may not be

demographically representative. However for this study, it is young consumers being targeted,

making this an appropriate method for targeting the generation Y consumers and justifying the

choice of a survey administered online.

An online survey administered via the social networking website Facebook, distributed

amongst the researchers group of friends, was determined as the most appropriate medium to

target generation Y consumers on. Facebooka popular social networking site is popular

with young people in the UK and is part of the social networking phenomenon. At July 2010,

more than 26 million people from the UK were registered with Facebook, which is more than 42

percent of the population (InternetWorldStats, 2010). This justifies choosing this platform in

administering the survey. The survey was piloted amongst 20 people in order to improve content

validityall piloted respondents were between the ages of 18 to 34. The survey was piloted in

order to ensure it was intuitive and the content made sense, in addition to ensuring there were no

technical issues with the website. When the researcher received the feedback that the survey was

operational, it was posted on the website page and the data collection period began, and lastedfor three weeks.


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3.7 Data preparation and analysis

In order to ensure the completion of all answers in the online survey, the researcher

configured the settings so survey could not be completed and published until every answer had a

response/value. This eliminated the common problem that researchers can often face when

preparing data for analysis, as often all responses require screening in order to remove invalid

responses, if certain answers do not have a value for instance, which can be costly in terms of

time (Bryman & Bell, 2007).

The website used for the creation of the survey had analytical functionality which

allowed for the creation of charts and graphs based on results for the entire number of responses

or specified characteristics of the respondent. It facilitated a cross-tabulation function which

enabled the comparison of specific results of a particular question to other questions. For

example, all respondents who are male could be isolated in order to identify whether one gender

was typically more interested in m-banking than the other. The quantitative data were exported

to Microsoft Excel spreadsheet software for analysis. The sole purpose of exporting the data to

Excel was to conduct Chi-Square tests, in order to test for statistical significance i.e. identifying

whether there are associations or differences existing between variables. The cross-tabulation

allowed for the effective illustration of relationships existing between variables however, the

Chi-Square identified whether these relationships were statistically significantly, or if they were

the result of sampling errors. Data and results were presented in a combination of charts and

tables, created via the survey website, which the researcher deemed as being simple to

comprehend for the reader. In addition a written analytical explanation accompanying every

chart and graph was provided.

3.7.1 Testing for significance

As part of the data analysis, certain sets of data were tested for statistical significance. In

general, an assumption is made when testing hypotheses that the null hypothesis (H0) is true.

Therefore, it is assumed that there is no association between variables and any links or

differences observed are the result of sampling error (Bryman & Bell, 2007). A key role in the

empirical testing of hypotheses in marketing research is testing for statistical significance. Chi-

Square tests are a method for evaluating significant differences between nominal data sets

(Bryman & Bell, 2007), and will be employed as the sole statistical analysis method in thisstudy. The remaining analysis conducted in this study will be based on the observation of results


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through the cross-tabulation of data sets. Based on the cross-tabulation of data, charts and tables

will be created in order to illustrate results and serve as a tool aiding the analysis.

The researcher created a standardised spreadsheet in Microsoft Excel allowing for all the

relevant calculations to be made automatically after the manual input of any set of cross-

tabulated, observed data from respondents. The researcher achieved this through the input of

formula into specific cells and utilising Excels built in CHITEST functionality. The Chi-

Square statistic, degrees of freedom (d.o.f.) of the data set and the probability-value (p-value)

were the outputs of this spreadsheet. The p-value could then be compared to the employed

significance level of this study which is 0.05, as the acceptable level of significance for most

applications is 0.05 (Zikmund & Babin, 2007). If the calculated significance level (i.e. p-value)

is less than the employed significance level (i.e. < 0.05), the hypothesis about links or differences

existing between variables is supported (Zikmund & Babin, 2007), therefore resulting in the H 0

being rejected. If the calculated p-value is more than the 0.05 the H0 is supported, meaning any

differences identified in the data are a result of sampling error.

3.8 Assumptions made in research

A number of assumptions were made in the research stage pertaining to the respondents

who were completing the survey. Although respondents were not asked these questions directly

it was assumed that all respondents currently live in the UK and have a bank account. In order

for consumers to adopt m-banking they must have a bank account, otherwise their opinion is

invalid. Additionally, in order for the respondents responses to be of relevance to the study, they

must also live in the UK, as this study aims to identify consumer perceptions of m-banking in the

UK retail banking sector. Additionally, respondents were assumed to be competent in IT and

electronic-banking based on their usage of Internet banking. Similarly, assumptions were made

that a respondent was competent in using mobile devices if they owned a smart-phone, as this is

an advanced handset. Relationships will be identified in the analysis chapter between

respondents usage of Internet banking and their current usage of m-banking in addition to

intention to adopt m-banking the same relationships will be identified between those

respondents who own smart-phones, in addition to respondents gender and income.


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4.2 Survey sample

As detailed in the methodology section of the last chapter, a non-probability convenience

sample was conducted involving snowball and quota sampling methods. The researcher aimed to

achieve over 150 responses to the online survey, and was prepared to contact further accessible

people, as a contingency plan in the event that the survey did not have enough respondents.

However, at the end of the data collection period the survey had 281 responses in total. Out of

the 281 responses received, 5 (1.78%) were ineligible due to the respondents falling out with the

age brackets imposed by the researcher due to the study focussing on generation Y co nsumers,

who are aged 18-34. No data was incomplete due to the questionnaire being configured in way

that did not allow completion and submission of the survey with missing data. As a result, the

overall sample size used in analysis was 276 (n = 276).

4.3 A descriptive overview

nick fleming

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